Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production

Ibrahim, N. S.; Alharbi, Ali A; Alzahrani, Turki A; Abdulkarim, Abdullah M; Alessa, Ibrahim A; Hameed, Abdullah M; Albabtain, Abdullaziz S; Alqahtani, Deemah; Alsawwaf, Mohammad; Almuqhim, Abdullah A

doi:10.3390/s22145326

Cited by 17 publications

(8 citation statements)

References 34 publications

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“…Ibrahim et al [112] delved into the temporal prediction of corrosion defect depth in pipelines by classification of the oil, gas, and water from 1,968 samples from O&G production Saudi Aramco of five well reservoirs with few parameters location, contact, permeability average, volume, production, wellhead and bottom hole pressure, and ratio. This study uses a variety of AI models, including XGBoost, ANN, RNN, MLR, Polynomial Linear Regression (PLR), SVR, Decision Tree Regression (DTR), and RF Regression (RFR).…”

Section: Application Of Interrelated Ai Modelsmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

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Section: Application Of Interrelated Ai Modelsmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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“…Chen Tianqi designed the extreme gradient boosting tree (XGBoost), and the core of the XGBoost algorithm is the integration of multiple weak learners to build a single strong learner by progressively optimizing the loss function [40,41]. The input samples of each decision tree and its predecessor tree are trained to correlate with the prediction results, and finally, the prediction results of all the decision trees are accumulated as the final prediction results.…”

Section: Principle Of Extreme Gradient Boosting Treementioning

confidence: 99%

Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method

Zhang,

Wang,

Jia

et al. 2024

Applied Sciences

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The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources. In order to optimize the prediction methods of porosity and permeability and better guide gas field development, it is necessary to identify the most effective approaches. Therefore, based on the extreme gradient boosting (XGBoost) algorithm, laboratory test data of the porosity and permeability of cores from the southern margin of the Ordos Basin were selected as the target labels, conventional logging curves were used as the input feature variables, and the mean absolute error (MAE) and the coefficient of determination (R2) were used as the evaluation indicators. Following the selection of the optimal feature variables and optimization of the hyper-parameters, an XGBoost porosity and permeability prediction model was established. Subsequently, the innovative application of homogeneous clustering (K-means) data preprocessing was applied to enhance the XGBoost model’s performance. The results show that logarithmically preprocessed (LOG(PERM)) target labels enhanced the performance of the XGBoost permeability prediction model, with an increase of 0.26 in its test set R2. Furthermore, the application of K-means improved the performance of the XGBoost prediction model, with an increase of 0.15 in the R2 of the model and a decrease of 0.017 in the MAE. Finally, the POR_0/POR_1 grouped porosity model was selected as the final predictive model for porosity in the study area, and the Arctan(PERM)_0/Arctan(PER0M)_1 grouped model was selected as the final predictive model for permeability, which has better prediction accuracy than logging curves. The combination of K-means and the XGBoost modeling method provides a new approach and reference for the efficient and relatively accurate evaluation of porosity and permeability in the study area.

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“…Propose an alternative way by making this step much more accessible and using less computing power. ML is the solution to forecast Oil and Gas production with high precision and speed; it can use the dataset's features to estimate how much Oil and Gas will be produced [9]. According to the World Energy Report, an estimated 30% of the world's energy came from fossil fuels and natural Gas in 2020.…”

Section: Introductionmentioning

confidence: 99%

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

A. Al shabaan,

N. Nemer

2024

J. Al-Qadisiyah Comp. Sci. Math.

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Oil and gas production forecasting has always been a hot topic in the petroleum industry. Production forecasting in this sector aims to estimate future production rates, facilitating operational planning, production optimization, and resource allocation for companies. Scientists have traditionally attempted to forecast oil and gas production using methods such as Numerical Reservoir Simulation (NRS) and Decline Curve Analysis (DCA). However, these methods present challenges including time-consuming processes lasting hours or even days, uncertain accuracy, reliance on accurate static models, and uncertainty in dynamic model parameters. In this research, aim to address these limitations by leveraging machine learning models for production forecasting. These models enable faster and more precise decision-making by accurately predicting future outcomes based on historical data. Our study employs three models: Decision Trees (DTR), Random Forest (RFR), and XGBoost. In this reserch utilize the Python programming language and a dataset from wells in New York State, USA. Experimental results demonstrate that the RFR model achieves the highest accuracy (99%) for oil and gas production compared to the XGBoost and DTR models.

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Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production

Cited by 17 publications

References 34 publications

Predictive Analytics Performance on Oil and Gas: A Significant Review

Predictive Analytics Performance on Oil and Gas: A Significant Review

Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

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